Patient treatment from the initial diagnosis until the final patient discharge may often involve very complex and involved clinical processes. The clinical process for a particular type of treatment may include hundreds of different activities that are performed by a wide variety of actors within the healthcare environment. Because of the complexity of some clinical processes, there are often many opportunities for optimization to improve the quality, delivery, and cost of healthcare. However, the complexity of clinical processes also often makes it difficult to identify the opportunities that will have the greatest impact on improving the outcomes of the processes in an efficient manner.
A number of different approaches have been taken in an attempt to improve clinical processes within healthcare facilities. For instance, one such approach is transformational consulting. Under this approach, consultants evaluate a clinical facility's current practice for a particular clinical process. The consultants then attempt to identify areas within the facility's current clinical process that require improvement. Based on those identifications, the consultants then attempt to develop changes to the clinical process that may be implemented to improve the process. This may often involve working with the client to determine “on the fly” what changes are appropriate to address the shortcomings of the current clinical process. However, this consulting process is an inefficient approach that is time consuming and labor intensive. Moreover, this approach focuses primarily on the facility's current clinical process, potentially ignoring many opportunities for improvement.
Management information systems have also played a role in attempts to improve clinical processes. These systems allow healthcare personnel to collect, track, and analyze a wide variety of clinical data from healthcare facilities. While the collection and analysis of such data may be helpful, there are a number of limitations to the flexibility and sophistication of current clinical management systems. For example, although management information systems allow healthcare facilities to gather a wide range of data, some systems may not permit modeling or simulation of the effect of proposed changes to current clinical procedures. Other systems that do permit a user to predict or simulate outcomes from process changes may do so based only on the internally generated clinical data sets that are unconstrained by other objective guidelines.
To address the shortcomings of many management information systems, evidence-based modeling of clinical operations has been proposed. Under this approach, effects on outcomes may be evaluated by comparing empirical data accessed from clinical facilities to objective guidelines or criteria. However, this approach also poses a number of limitations. For instance, the objective guidelines or criteria used are merely individual pieces of information that are independent of an entire clinical process. Accordingly, such an approach may fail to account for a change's effect on the entire clinical process, such as any impact to other activities within the process. Further, such systems do not readily provide the ability to efficiently analyze and prioritize clinical process improvements.